





'''simple docstring'''

import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    PNDMScheduler,
    StableDiffusionLDMaDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS


enable_full_determinism()






class   UpperCAmelCase      (			unittest.TestCase							):
 __lowercase  						=							StableDiffusionLDMaDPipeline
 __lowercase  						=							TEXT_TO_IMAGE_PARAMS
 __lowercase  						=							TEXT_TO_IMAGE_BATCH_PARAMS
 __lowercase  						=							TEXT_TO_IMAGE_IMAGE_PARAMS
 def 	UpperCAmelCase_				(							self :int				)->				Optional[int]:
     torch.manual_seed(0				)
     A__							=     UNetaDConditionModel(
         block_out_channels=(32, 64)		,			layers_per_block=2		,			sample_size=32		,			in_channels=4		,			out_channels=4		,			down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")		,			up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")		,			cross_attention_dim=32		,			)
     A__							=     DDIMScheduler(
         beta_start=0.0_0_0_8_5		,			beta_end=0.0_1_2		,			beta_schedule="scaled_linear"		,			clip_sample=lowercase_		,			set_alpha_to_one=lowercase_		,			)
     torch.manual_seed(0				)
     A__							=     AutoencoderKL(
         block_out_channels=[32, 64]		,			in_channels=6		,			out_channels=6		,			down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]		,			up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]		,			latent_channels=4		,			)
     torch.manual_seed(0				)
     A__							=     CLIPTextConfig(
         bos_token_id=0		,			eos_token_id=2		,			hidden_size=32		,			intermediate_size=37		,			layer_norm_eps=1E-05		,			num_attention_heads=4		,			num_hidden_layers=5		,			pad_token_id=1		,			vocab_size=10_00		,			)
     A__							=     CLIPTextModel(lowercase_				)
     A__							=     CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"				)

     A__							=     {
         "unet": unet,
         "scheduler": scheduler,
         "vae": vae,
         "text_encoder": text_encoder,
         "tokenizer": tokenizer,
         "safety_checker": None,
         "feature_extractor": None,
     }
     return components
 def 	UpperCAmelCase_				(							self :List[Any]		,			lowercase_ :List[str]		,			lowercase_ :int=0				)->				int:
     if str(lowercase_				).startswith("mps"				):
         A__							=     torch.manual_seed(lowercase_				)
     else:
         A__							=     torch.Generator(device=lowercase_				).manual_seed(lowercase_				)
     A__							=     {
         "prompt": "A painting of a squirrel eating a burger",
         "generator": generator,
         "num_inference_steps": 2,
         "guidance_scale": 6.0,
         "output_type": "numpy",
     }
     return inputs
 def 	UpperCAmelCase_				(							self :str				)->				Dict:
     A__							=     "cpu"  # ensure determinism for the device-dependent torch.Generator

     A__							=     self.get_dummy_components()
     A__							=     StableDiffusionLDMaDPipeline(**lowercase_				)
     A__							=     ldmad_pipe.to(lowercase_				)
     ldmad_pipe.set_progress_bar_config(disable=lowercase_				)

     A__							=     self.get_dummy_inputs(lowercase_				)
     A__							=     ldmad_pipe(**lowercase_				)
     A__,      A__							=     output.rgb, output.depth

     A__							=     rgb[0, -3:, -3:, -1]
     A__							=     depth[0, -3:, -1]

     assert rgb.shape == (1, 64, 64, 3)
     assert depth.shape == (1, 64, 64)

     A__							=     np.array(
         [0.3_7_3_3_8_1_7_6, 0.7_0_2_4_7, 0.7_4_2_0_3_1_9_3, 0.5_1_6_4_3_6_0_4, 0.5_8_2_5_6_7_9_3, 0.6_0_9_3_2_1_3_6, 0.4_1_8_1_0_9_5, 0.4_8_3_5_5_8_7_7, 0.4_6_5_3_5_2_6_2]				)
     A__							=     np.array([1_0_3.4_6_7_2_7, 8_5.8_1_2_0_0_4, 8_7.8_4_9_2_3_6]				)

     assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb				).max() < 1E-2
     assert np.abs(image_slice_depth.flatten() - expected_slice_depth				).max() < 1E-2
 def 	UpperCAmelCase_				(							self :int				)->				Dict:
     A__							=     self.get_dummy_components()
     A__							=     StableDiffusionLDMaDPipeline(**lowercase_				)
     A__							=     ldmad_pipe.to(lowercase_				)
     ldmad_pipe.set_progress_bar_config(disable=lowercase_				)

     A__							=     self.get_dummy_inputs(lowercase_				)
     A__							=     3 * [inputs["prompt"]]

     # forward
     A__							=     ldmad_pipe(**lowercase_				)
     A__,      A__							=     output.rgb, output.depth
     A__							=     rgb_slice_a[0, -3:, -3:, -1]
     A__							=     depth_slice_a[0, -3:, -1]

     A__							=     self.get_dummy_inputs(lowercase_				)
     A__							=     3 * [inputs.pop("prompt"				)]

     A__							=     ldmad_pipe.tokenizer(
         lowercase_		,			padding="max_length"		,			max_length=ldmad_pipe.tokenizer.model_max_length		,			truncation=lowercase_		,			return_tensors="pt"		,			)
     A__							=     text_inputs["input_ids"].to(lowercase_				)

     A__							=     ldmad_pipe.text_encoder(lowercase_				)[0]

     A__							=     prompt_embeds

     # forward
     A__							=     ldmad_pipe(**lowercase_				)
     A__,      A__							=     output.rgb, output.depth
     A__							=     rgb_slice_a[0, -3:, -3:, -1]
     A__							=     depth_slice_a[0, -3:, -1]

     assert np.abs(rgb_slice_a.flatten() - rgb_slice_a.flatten()				).max() < 1E-4
     assert np.abs(depth_slice_a.flatten() - depth_slice_a.flatten()				).max() < 1E-4



 def 	UpperCAmelCase_				(							self :str				)->				List[str]:
     A__							=     "cpu"  # ensure determinism for the device-dependent torch.Generator
     A__							=     self.get_dummy_components()
     A__							=     PNDMScheduler(skip_prk_steps=lowercase_				)
     A__							=     StableDiffusionLDMaDPipeline(**lowercase_				)
     A__							=     ldmad_pipe.to(lowercase_				)
     ldmad_pipe.set_progress_bar_config(disable=lowercase_				)

     A__							=     self.get_dummy_inputs(lowercase_				)
     A__							=     "french fries"
     A__							=     ldmad_pipe(**lowercase_		,			negative_prompt=lowercase_				)

     A__,      A__							=     output.rgb, output.depth
     A__							=     rgb[0, -3:, -3:, -1]
     A__							=     depth[0, -3:, -1]

     assert rgb.shape == (1, 64, 64, 3)
     assert depth.shape == (1, 64, 64)

     A__							=     np.array(
         [0.3_7_0_4_4, 0.7_1_8_1_1_5_0_3, 0.7_2_2_3_2_5_1, 0.4_8_6_0_3_6_7_5, 0.5_6_3_8_3_9_1, 0.6_3_6_4_9_4_8, 0.4_2_8_3_3_7_0_4, 0.4_9_0_1_3_1_5, 0.4_7_9_2_6_2_1_7]				)
     A__							=     np.array([1_0_7.8_4_7_3_8, 8_4.6_2_8_0_2, 8_9.9_6_2_1_3_5]				)
     assert np.abs(rgb_slice.flatten() - expected_slice_rgb				).max() < 1E-2
     assert np.abs(depth_slice.flatten() - expected_slice_depth				).max() < 1E-2






@slow
@require_torch_gpu
class   UpperCAmelCase      (			unittest.TestCase							):
 def 	UpperCAmelCase_				(							self :List[str]				)->				int:
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()
 def 	UpperCAmelCase_				(							self :Union[str, Any]		,			lowercase_ :Union[str, Any]		,			lowercase_ :int="cpu"		,			lowercase_ :Optional[int]=torch.floataa		,			lowercase_ :List[str]=0				)->				str:
     A__							=     torch.Generator(device=lowercase_				).manual_seed(lowercase_				)
     A__							=     np.random.RandomState(lowercase_				).standard_normal((1, 4, 64, 64)				)
     A__							=     torch.from_numpy(lowercase_				).to(device=lowercase_		,			dtype=lowercase_				)
     A__							=     {
         "prompt": "a photograph of an astronaut riding a horse",
         "latents": latents,
         "generator": generator,
         "num_inference_steps": 3,
         "guidance_scale": 7.5,
         "output_type": "numpy",
     }
     return inputs



 def 	UpperCAmelCase_				(							self :Dict				)->				Optional[int]:
     A__							=     StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d"				)
     A__							=     ldmad_pipe.to(lowercase_				)
     ldmad_pipe.set_progress_bar_config(disable=lowercase_				)

     A__							=     self.get_inputs(lowercase_				)
     A__							=     ldmad_pipe(**lowercase_				)
     A__,      A__							=     output.rgb, output.depth
     A__							=     rgb[0, -3:, -3:, -1].flatten()
     A__							=     rgb[0, -3:, -1].flatten()

     assert rgb.shape == (1, 5_12, 5_12, 3)
     assert depth.shape == (1, 5_12, 5_12)

     A__							=     np.array(
         [0.5_3_8_0_5_4_6_5, 0.5_6_7_0_7_3_0_5, 0.5_4_8_6_5_1_5, 0.5_7_0_1_2_2_3_6, 0.5_8_1_4_5_1_1, 0.5_6_2_5_3_4_8_7, 0.5_4_8_4_3_0_1_4, 0.5_5_0_9_2_2_6_3, 0.6_4_5_9_7_0_6]				)
     A__							=     np.array(
         [0.9_2_6_3_7_8_1, 0.6_6_7_8_6_7_2, 0.5_4_8_6_5_1_5, 0.9_2_2_0_2_1_4_5, 0.6_7_8_3_1_1_3_5, 0.5_6_2_5_3_4_8_7, 0.9_2_4_1_6_9_4, 0.7_5_5_1_4_7_8, 0.6_4_5_9_7_0_6]				)
     assert np.abs(rgb_slice - expected_slice_rgb				).max() < 3E-3
     assert np.abs(depth_slice - expected_slice_depth				).max() < 3E-3






@nightly
@require_torch_gpu
class   UpperCAmelCase      (			unittest.TestCase							):
 def 	UpperCAmelCase_				(							self :Union[str, Any]				)->				List[str]:
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()
 def 	UpperCAmelCase_				(							self :List[str]		,			lowercase_ :Any		,			lowercase_ :Optional[int]="cpu"		,			lowercase_ :Union[str, Any]=torch.floataa		,			lowercase_ :int=0				)->				Optional[Any]:
     A__							=     torch.Generator(device=lowercase_				).manual_seed(lowercase_				)
     A__							=     np.random.RandomState(lowercase_				).standard_normal((1, 4, 64, 64)				)
     A__							=     torch.from_numpy(lowercase_				).to(device=lowercase_		,			dtype=lowercase_				)
     A__							=     {
         "prompt": "a photograph of an astronaut riding a horse",
         "latents": latents,
         "generator": generator,
         "num_inference_steps": 50,
         "guidance_scale": 7.5,
         "output_type": "numpy",
     }
     return inputs
 def 	UpperCAmelCase_				(							self :Tuple				)->				Dict:
     A__							=     StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d"				).to(lowercase_				)
     ldmad_pipe.set_progress_bar_config(disable=lowercase_				)

     A__							=     self.get_inputs(lowercase_				)
     A__							=     ldmad_pipe(**lowercase_				)
     A__,      A__							=     output.rgb, output.depth

     A__							=     0.4_9_5_5_8_6
     A__							=     0.3_3_7_9_5_5_1_5
     A__							=     1_1_2.4_8_5_1_8
     A__							=     9_8.4_8_9_7_4_6
     assert np.abs(expected_rgb_mean - rgb.mean()				) < 1E-3
     assert np.abs(expected_rgb_std - rgb.std()				) < 1E-3
     assert np.abs(expected_depth_mean - depth.mean()				) < 1E-3
     assert np.abs(expected_depth_std - depth.std()				) < 1E-3



 def 	UpperCAmelCase_				(							self :Any				)->				Optional[int]:
     A__							=     StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d-4c"				).to(lowercase_				)
     ldmad_pipe.set_progress_bar_config(disable=lowercase_				)

     A__							=     self.get_inputs(lowercase_				)
     A__							=     ldmad_pipe(**lowercase_				)
     A__,      A__							=     output.rgb, output.depth

     A__							=     0.4_1_9_4_1_2_7
     A__							=     0.3_5_3_7_5_5_8_6
     A__							=     0.5_6_3_8_5_0_2
     A__							=     0.3_4_6_8_6_1_0_3

     assert rgb.shape == (1, 5_12, 5_12, 3)
     assert depth.shape == (1, 5_12, 5_12, 1)
     assert np.abs(expected_rgb_mean - rgb.mean()				) < 1E-3
     assert np.abs(expected_rgb_std - rgb.std()				) < 1E-3
     assert np.abs(expected_depth_mean - depth.mean()				) < 1E-3
     assert np.abs(expected_depth_std - depth.std()				) < 1E-3




"""simple docstring"""



from __future__ import annotations

import math



def 	_lowerCamelCase(	a   ,    a				):
       __a       =    u
       for i in range(1   ,    a				):
              __a       =    temp * (u - i)
       return temp



def 	_lowerCamelCase(	):
       __a       =    int(input("enter the numbers of values: "				)				)
       __a       =    []
       for _ in range(a				):
              y.append([]				)
       for i in range(a				):
              for j in range(a				):
                     y[i].append(a				)
                     __a       =    0

       print("enter the values of parameters in a list: "				)
       __a       =    list(map(a   ,    input().split()				)				)

       print("enter the values of corresponding parameters: "				)
       for i in range(a				):
              __a       =    float(input()				)

       __a       =    int(input("enter the value to interpolate: "				)				)
       __a       =    (value - x[0]) / (x[1] - x[0])

       # for calculating forward difference table

       for i in range(1   ,    a				):
              for j in range(n - i				):
                     __a       =    y[j + 1][i - 1] - y[j][i - 1]

       __a       =    y[0][0]
       for i in range(1   ,    a				):
              summ += (ucal(a   ,    a				) * y[0][i]) / math.factorial(a				)

       print(F"the value at {value} is {summ}"				)


if __name__ == "__main__":
   main()
